# Kalshi Weather Markets: Prices vs Forecast Odds (`scrapemint/kalshi-weather-markets`) Actor

Every open Kalshi weather market: daily high and low temperature in ~30 US cities, rain and snow. Live bid/ask and implied probability next to an 82-member ECMWF+GFS ensemble forecast probability for the exact settlement station, with today's observed high/low. $0.005 per market.

- **URL**: https://apify.com/scrapemint/kalshi-weather-markets.md
- **Developed by:** [Ken M](https://apify.com/scrapemint) (community)
- **Categories:** Business, News
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$5.00 / 1,000 weather markets

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Kalshi Weather Markets: Prices vs Forecast Odds

Every open **Kalshi weather market**, with the live price next to a **forecast probability** for the exact station the market settles on:

- **Daily high and low temperature** in ~30 US cities (New York, Chicago, Miami, Austin, Denver, LA, Philadelphia, Houston, Dallas, Boston, Atlanta, Seattle, Phoenix, Las Vegas...), today and tomorrow
- **Rain:** daily "will it rain" by city, weekend rain, and monthly totals
- **Snow:** monthly snowfall
- **Everything else** in Kalshi's Climate and Weather category (hurricanes, ski-resort openings, climate), price only

For each bracket you get bid, ask, **implied probability**, volume and open interest. Next to it you get **forecastProbability**: the share of 82 ensemble members (ECMWF 51 + GFS 31) that land in that bracket, centred on the National Blend of Models at the station. You also get **edge** (forecast minus market) and, for today's markets, the **high or low already recorded** at the station.

No Kalshi account, no API key, no proxy. $0.005 per market, no start fee.

### What you get

`type`, `city`, `eventTitle`, `date` or `month`, `bracket`, `strikeType`, `floorStrike`, `capStrike`, `yesBid`, `yesAsk`, `lastPrice`, `impliedProbability`, `forecastProbability`, `edge`, `forecastMean`, `observedSoFar`, `ensembleMembers`, `forecastCentredOn`, `forecastNote`, `station`, `stationName`, `volume`, `volume24h`, `openInterest`, `closeTime`, `marketTicker`, `eventTicker`, `seriesTicker`, `rules`, `url`

### Example input

Today's and tomorrow's temperature brackets in three cities:

```json
{
  "marketTypes": ["high_temp", "low_temp"],
  "cities": ["New York City", "Chicago", "Miami"]
}
```

Every rain market, only the ones that have traded:

```json
{
  "marketTypes": ["rain"],
  "onlyTraded": true
}
```

One series by ticker, prices only:

```json
{
  "seriesTickers": ["KXHIGHNY"],
  "includeForecast": false
}
```

### Example output

```json
{
  "type": "high_temp",
  "city": "New York City",
  "date": "2026-10-18",
  "bracket": "56° to 57°",
  "yesBid": 0.46,
  "yesAsk": 0.48,
  "impliedProbability": 0.47,
  "forecastProbability": 0.32,
  "edge": -0.14,
  "forecastMean": 55.8,
  "ensembleMembers": 82,
  "forecastCentredOn": "NBM",
  "station": "KNYC",
  "stationName": "New York City, Central Park",
  "volume": 27145.67,
  "openInterest": 17014.31,
  "marketTicker": "KXHIGHNY-26OCT18-B56.5",
  "url": "https://kalshi.com/markets/kxhighny"
}
```

### Uses

- **Weather traders:** scan every city's brackets in one run and sort by `edge`
- **Bots and dashboards:** schedule it every 15-60 minutes for a time series of prices against the forecast
- **Research:** compare how Kalshi prices moved against the ensemble before settlement

### Pricing

- **$0.005** per market row
- No start fee. A run that finds nothing is free.

### How the forecast works

1. The settlement station comes from the market's rules, e.g. "recorded at New York City (CLINYC)" is Central Park, KNYC. Its coordinates come from the National Weather Service.
2. The ensemble's daily high/low for that point is shifted so its mean sits on the **National Blend of Models**, the bias-corrected forecast NWS forecasts are built from. The ensemble's spread is kept.
3. Each member gets ±1.5°F of station-level error. It is then rounded to whole degrees, because that is what the NWS climate report publishes.
4. For **today**, the station's observations since midnight local standard time (the report's day) set a floor for the high and a ceiling for the low.
5. **Monthly rain/snow** adds the month so far to each member's remaining days. This only works when the month ends within the 16-day forecast range. Otherwise `forecastProbability` is empty and `forecastNote` says why.

### Notes

- `forecastProbability` is a statistical estimate, not advice. Markets often know things the models don't, such as a front arriving early.
- Hourly observations can miss the exact peak between readings, so `observedSoFar` can be a degree under the final reported high.
- Markets outside the US (Paris, London...) use Open-Meteo's best model at the city centre instead of a station.
- "Other" markets (hurricanes, ski openings...) come with prices only.

# Actor input Schema

## `marketTypes` (type: `array`):

Which weather markets to read. high_temp and low_temp are the daily temperature brackets; rain covers daily, weekend and monthly rain; snow is monthly snowfall; other is hurricanes, ski-resort openings, climate and the rest (price only, no forecast).

## `cities` (type: `array`):

Optional. Only these cities, e.g. New York City, Chicago, Miami, Austin, Denver. Empty = every city.

## `seriesTickers` (type: `array`):

Optional. Exact Kalshi series, e.g. KXHIGHNY, KXLOWTCHI, KXRAINNYCM. Overrides market types and cities.

## `includeForecast` (type: `boolean`):

Adds the share of 82 ensemble forecast members (ECMWF + GFS) that land in each bracket at the settlement station, the edge versus the market, and today's observed high/low. Off = prices only, faster.

## `onlyTraded` (type: `boolean`):

Skip markets with no volume and no open interest.

## `maxRows` (type: `integer`):

Stop after this many market rows.

## Actor input object example

```json
{
  "marketTypes": [
    "high_temp",
    "low_temp"
  ],
  "cities": [],
  "seriesTickers": [],
  "includeForecast": true,
  "onlyTraded": false,
  "maxRows": 500
}
```

# Actor output Schema

## `rows` (type: `string`):

Bracket, bid/ask, implied probability, forecast probability, edge, observed so far, volume.

## `summary` (type: `string`):

Rows per market type and any series that failed.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "marketTypes": [
        "high_temp",
        "low_temp"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("scrapemint/kalshi-weather-markets").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "marketTypes": [
        "high_temp",
        "low_temp",
    ] }

# Run the Actor and wait for it to finish
run = client.actor("scrapemint/kalshi-weather-markets").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "marketTypes": [
    "high_temp",
    "low_temp"
  ]
}' |
apify call scrapemint/kalshi-weather-markets --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,scrapemint/kalshi-weather-markets"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/uBYHI4WObiYLmugUG/builds/ypIolw6gnVvW9sXbE/openapi.json